arXiv Machine Learning June 5, 2026 By Dmitry Zaytsev, Valentina Kuskova, Michael Coppedge

From Causal Discovery to Dynamic Causal Inference in Neural Time Series

Read the original on arXiv Machine Learning →

arXiv:2603. 20980v3 Announce Type: replace Abstract: Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the underlying causal network is known a priori - an assumption rarely satisfied in real-world domains where causal structure is uncertain, evolving, or only indirectly observable.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 4

Large Causal Models for Temporal Causal Discovery

arXiv:2602. 18662v2 Announce Type: replace Abstract: Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for each individual dataset.

By Nikolaos Kougioulis, Nikolaos Gkorgkolis, MingXue Wang, Bora Caglayan, Dario Simionato, Andrea Tonon, Ioannis Tsamardinos

benchmarks

arXiv AI

Jun 17

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes

arXiv:2507. 11178v3 Announce Type: replace-cross Abstract: With the advancement of deep learning technologies, various neural network-based Granger causality models have been proposed.

By Meiliang Liu, Huiwen Dong, Xiaoxiao Yang, Yunfang Xu, Mingbao Yang, Zijin Li, Zhengye Si, Xinyue Yang, Zhiwen Zhao

efficiency

arXiv Machine Learning

Jun 3

Learning Temporal Causal Structure via Smooth Differentiable Optimization

arXiv:2606. 03227v1 Announce Type: new Abstract: Causal discovery with instantaneous effects in multivariate time series is challenging, as the instantaneous structure must be acyclic.

By Tong Zhao, Ce Guo, Wayne Luk, Emil Lupu, Ray Dipojjwal

benchmarks

arXiv AI

Jun 10

Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting

arXiv:2606. 10607v1 Announce Type: cross Abstract: Causal discovery aims to uncover causal structures from observational data, which is crucial for real-world decision-making.

By Xinyu Li, Yuanyuan Wang, Haoxuan Li, Chuan Zhou, Erdun Gao, Bo Han, Tongliang Liu, Kun Zhang, Howard Bondell, Mingming Gong

llms agents safety

arXiv AI

Jul 14

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.

By Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen, Pengfei Hua, Boyan Xu, Zhengming Chen, Zhifeng Hao, Peng Cui

arXiv Machine Learning

Jun 15

Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretraining

arXiv:2605. 26759v2 Announce Type: replace Abstract: Causal discovery from time series is critical for many real-world applications, such as tracing the root causes of anomalies.

By Biao Ouyang, Tengxue Zhang, Zhihao Zhuang, Yang Shu, Chenjuan Guo, Bin Yang

Related stories

Large Causal Models for Temporal Causal Discovery

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes

Learning Temporal Causal Structure via Smooth Differentiable Optimization

Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretraining